Soft Tissue Edema Around Musculoskeletal Sarcomas at Magnetic Resonance Imaging

The presence of soft tissue edema around a malignant musculoskeletal neoplasm can interfere with accurate local tumor staging at magnetic resonance imaging. This article discusses and illustrates such edema, emphasizing means for avoiding misinterpretation of edema and subsequent overstaging

MR Imaging of Bone Marrow in Patients with Musculoskeletal Tumors

Knowledge of the appearances of normal bone marrow, metastases involving marrow, and therapy-related marrow changes shown by MR imaging is necessary in order to avoid misdiagnosis. This article reviews MR imaging techniques and the findings that allow distinction of normal yellow (fatty) marrow and red marrow from tumor in marrow, as well as the identification of marrow changes resulting from radiation therapy or treatment with marrow-stimulating drugs in patients with musculoskeletal tumors

Assessment of Neurovascular Involvement by Malignant Musculoskeletal Tumors

Determining the presence or absence of neurovascular involvement by a malignant musculoskeletal neoplasm is an important aspect of local tumor staging. This article discusses issues concerning such assessments made by diagnostic imaging techniques, including factors inherent to the patient and those related to imaging technology. The distinction between tumor contact and tumor encasement is emphasized and illustrated

Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings

Objectives: Radiomics utilizes quantitative image features (QIFs) to characterize tumor phenotype. In practice, radiological images are obtained from different vendors’ equipment using various imaging acquisition settings. Our objective was to assess the inter-setting agreement of QIFs computed from CT images by varying two parameters, slice thickness and reconstruction algorithm. Materials and Methods: CT images from an IRB-approved/HIPAA-compliant study assessing thirty-two lung cancer patients were included for the analysis. Each scan’s raw data were reconstructed into six imaging series using combinations of two reconstruction algorithms (Lung[L] and Standard[S]) and three slice thicknesses (1.25mm, 2.5mm and 5mm), i.e., 1.25L, 1.25S, 2.5L, 2.5S, 5L and 5S. For each imaging-setting, 89 well-defined QIFs were computed for each of the 32 tumors (one tumor per patient). The six settings led to 15 inter-setting comparisons (combinatorial pairs). To reduce QIF redundancy, hierarchical clustering was done. Concordance correlation coefficients (CCCs) were used to assess inter-setting agreement of the non-redundant feature groups. The CCC of each group was assessed by averaging CCCs of QIFs in the group. Results: Twenty-three non-redundant feature groups were created. Across all feature groups, the best inter-setting agreements (CCCs>0.8) were 1.25S vs 2.5S, 1.25L vs 2.5L, and 2.5S vs 5S; the worst (CCCs0.8 across all imaging settings. Conclusions: Varying degrees of inter-setting disagreements of QIFs exist when features are computed from CT images reconstructed using different algorithms and slice thicknesses. Our findings highlight the importance of harmonizing imaging acquisition for obtaining consistent QIFs to study tumor imaging phonotype

iRECIST: how to do it

Background iRECIST for the objective monitoring of immunotherapies was published by the official RECIST working group in 2017. Main body Immune-checkpoint inhibitors represent one of the most important therapy advancements in modern oncology. They are currently used for treatment of multiple malignant diseases especially at advanced, metastatic stages which were poorly therapeutically accessible in the past. Promising results of recent studies suggest that their application will further grow in the near future, particularly when used in combination with chemotherapy. A challenging aspect of these immunotherapies is that they may show atypical therapy response patterns such as pseudoprogression and demonstrate a different imaging spectrum of adverse reactions, both of which are crucial for radiologists to understand. In 2017 the RECIST working group published a modified set of response criteria, iRECIST, for immunotherapy, based on RECIST 1.1 which was developed for cytotoxic therapies and adapted for targeted agents. Conclusion This article provides guidance for response assessment of oncologic patients under immunotherapy based on iRECIST criteria

Intersubject Regularity in the Intrinsic Shape of Human V1

Previous studies have reported considerable intersubject variability in the three-dimensional geometry of the human primary visual cortex (V1). Here we demonstrate that much of this variability is due to extrinsic geometric features of the cortical folds, and that the intrinsic shape of V1 is similar across individuals. V1 was imaged in ten ex vivo human hemispheres using high-resolution (200 μm) structural magnetic resonance imaging at high field strength (7 T). Manual tracings of the stria of Gennari were used to construct a surface representation, which was computationally flattened into the plane with minimal metric distortion. The instrinsic shape of V1 was determined from the boundary of the planar representation of the stria. An ellipse provided a simple parametric shape model that was a good approximation to the boundary of flattened V1. The aspect ration of the best-fitting ellipse was found to be consistent across subject, with a mean of 1.85 and standard deviation of 0.12. Optimal rigid alignment of size-normalized V1 produced greater overlap than that achieved by previous studies using different registration methods. A shape analysis of published macaque data indicated that the intrinsic shape of macaque V1 is also stereotyped, and similar to the human V1 shape. Previoud measurements of the functional boundary of V1 in human and macaque are in close agreement with these results

Eighteenth Year of the Gulf of Maine Environmental Monitoring Program

This report summarizes the metals and organic contaminant data associated with the collection and analyses of blue mussel (Mytilus edulis) tissue from selected sites along the Gulf of Maine coast during the 2008 sampling season. Contaminant monitoring is conducted by the Gulfwatch Program for the Gulf of Maine Council on the Marine Environment (GOMC). A subset of these data is compared with analytical results from earlier Gulfwatch monitoring (2001-2007). Statistical analyses are limited to descriptive measures of replicates from selected sampling sites and include: arithmetic means, and appropriate measures of variance. The primary purpose of this report is to present the current annual results, present graphical representation of spatial and temporal trends and identify potential outliers in order to provide investigators and other interested persons with contemporary information concerning water quality in the Gulf of Maine, as reflected by uptake into resident shellfish (mussels and clams)

Volumetry of low-contrast liver lesions with CT: Investigation of estimation uncertainties in a phantom study

Purpose: To evaluate the performance of lesion volumetry in hepatic CT as a function of various imaging acquisition parameters. Methods: An anthropomorphic abdominal phantom with removable liver inserts was designed for this study. Two liver inserts, each containing 19 synthetic lesions with varying diameter (6–40 mm), shape, contrast (10–65 HU), and both homogenous and mixed-density were designed to have background and lesion CT values corresponding to arterial and portal-venous phase imaging, respectively. The two phantoms were scanned using two commercial CT scanners (GE 750 HD and Siemens Biograph mCT) across a set of imaging protocols (four slice thicknesses, three effective mAs, two convolution kernels, two pitches). Two repeated scans were collected for each imaging protocol. All scans were analyzed using a matched-filter estimator for volume estimation, resulting in 6080 volume measurements across all of the synthetic lesions in the two liver phantoms. A subset of portal venous phase scans was also analyzed using a semi-automatic segmentation algorithm, resulting in about 900 additional volume measurements. Lesions associated with large measurement error (quantified by root mean square error) for most imaging protocols were considered not measurable by the volume estimation tools and excluded for the statistical analyses. Imaging protocols were grouped into distinct imaging conditions based on ANOVA analysis of factors for repeatability testing. Statistical analyses, including overall linearity analysis, grouped bias analysis with standard deviation evaluation, and repeatability analysis, were performed to assess the accuracy and precision of the liver lesion volume biomarker. Results: Lesions with lower contrast and size ≤10 mm were associated with higher measurement error and were excluded from further analysis. Lesion size, contrast, imaging slice thickness, dose, and scanner were found to be factors substantially influencing volume estimation. Twenty-four distinct repeatable imaging conditions were determined as protocols for each scanner with a fixed slice thickness and dose. For the matched-filter estimation approach, strong linearity was observed for all imaging data for lesions ≥20 mm. For the Siemens scanner with 50 mAs effective dose at 0.6 mm slice thickness, grouped bias was about −10%. For all other repeatable imaging conditions with both scanners, grouped biases were low (−3%–3%). There was a trend of increasing standard deviation with decreasing dose. For each fixed dose, the standard deviations were similar among the three larger slice thicknesses (1.25, 2.5, 5 mm for GE, 1.5, 3, 5 mm for Siemens). Repeatability coefficients ranged from about 8% to 75% and showed similar trend to grouped standard deviation. For the segmentation approach, the results led to similar conclusions for both lesion characteristic factors and imaging factors but with increasing magnitude in all the error metrics assessed. Conclusions: Results showed that liver lesion volumetry was strongly dependent on lesion size, contrast, acquisition dose, and their interactions. The overall performances were similar for images reconstructed with larger slice thicknesses, clinically used pitches, kernels, and doses. Conditions that yielded repeatable measurements were identified and they agreed with the Quantitative Imaging Biomarker Alliance’s (QIBA) profile requirements in general. The authors’ findings also suggest potential refinements to these guidelines for the tumor volume biomarker, especially for soft-tissue lesions